Mediterranean forest resilience to drought and climate change

Enhancing resilience to climate change is a key management goal for Mediterranean ecosystems. Typically, these management plans are based on ecological knowledge of species’ tolerances derived from local studies limited in time and space. Remote sensing provides opportunities to study resilience over larger scales, but the tools needed to quantify the resilience of forests to drought and evaluate the effectiveness of management plans remain limited. This thesis examines how freely available satellite data can be used to quantify changes in forest canopies in response to climate variability. Using a combination of time-series and break-point analyses of satellite imagery I resolve limitations in forest resilience estimation and show that, for Spanish woodlands, the relative water availability during and following drought events are important in driving the canopy greenness loss and recovery. I show that despite increasing aridity, and examples of localised die-back events, Spanish forests are mostly becoming denser, with only 12% of locations analysed declining in greenness over the 18-year study period. This work demonstrates the importance of large-scale remote sensing analyses for obtaining an objective perspective on drought impacts. The thesis then explores the potential of remote sensing to map tree species in a region of regenerating woodlands near Madrid, providing the information needed for a nuanced understanding of resilience. I found that tree classification using high-resolution airborne hyperspectral imagery was highly accurate, while species maps produced using Sentinel 2 imagery (multispectral data with 10-m spatial resolution) were less successful at identifying species, with average agreement of 64% with the airborne derived map. Following on from this work, I used areas with high classification agreement between the airborne and spaceborne species information to study the effect of species composition on forest responses to droughts. I identify contrasting responses of the canopy greenness and wood production to drought. Specifically, wood production was found to be more sensitive to changes in water availability than canopy greenness. For the oak species, wood production was mirrored by changes in canopy greenness, but black pines reduced their wood production during droughts without substantial reduction in canopy greenness. I investigate the differences between the species and the mixing effects further by studying foliar compositions during a dry summer in Spain. There were strong differences between pines and oaks in the stable isotope ratios of carbon, probably driven by underlying differences in water-use efficiency, and differences in the stable isotope ratios of nitrogen, probably driven by underlying differences in species’ investments in the photosynthetic apparatus. I conclude by highlighting the implications of my research for studying the relationships between diversity and ecosystem functioning from space.PhD scholarship from Cambridge International Trus

Forest biotechnology advances to support global bioeconomy

The world is shifting to an innovation economy and forest biotechnology can play a major role in the bio-economy by providing farmers, producers, and consumers with tools that can better advance this transition. First-generation or conventional biofuels are primarily produced from food crops and are therefore limited in their ability to meet challenges for petroleum-product substitution and climate change mitigation, and to overcome the food-versus-fuel dilemma. In the longer term, forest lignocellulosic biomass will provide a unique renewable resource for large-scale production of bioenergy, biofuels and bio-products. These second-generation or advanced biofuels and bio-products have also the potential to avoid many of the issues facing the first-generation biofuels, particularly the competition concerning land and water used for food production. To expand the range of natural biological resources the rapidly evolving tools of biotechnology can ameliorate the conversion process, lower the conversion costs and also enhance target yield of forest biomass feedstock and the product of interest. Therefore, linking forest biotechnology with industrial biotechnology presents a promising approach to convert woody lignocellulosic biomass into biofuels and bio-products. Major advances and applications of forest biotechnology that are being achieved to competitively position forest biomass feedstocks with corn and other food crops are outlined. Finally, recommendations for future work are discussed

UAV-based LiDAR for high-throughput determination of plant height and above‐ground biomass of the bioenergy grass arundo donax

Replacing fossil fuels with cellulosic biofuels is a valuable component of reducing the drivers of climate change. This leads to a requirement to develop more productive bioenergy crops, such as Arundo donax with the aim of increasing above-ground biomass (AGB). However, direct measurement of AGB is time consuming, destructive, and labor-intensive. Phenotyping of plant height and biomass production is a bottleneck in genomics- and phenomics-assisted breeding. Here, an unmanned aerial vehicle (UAV) for remote sensing equipped with light detection and ranging (LiDAR) was tested for remote plant height and biomass determination in A. donax. Experiments were conducted on three A. donax ecotypes grown in well-watered and moderate drought stress conditions. A novel UAV-LiDAR data collection and processing workflow produced a dense three-dimensional (3D) point cloud for crop height estimation through a normalized digital surface model (DSM) that acts as a crop height model (CHM). Manual measurements of crop height and biomass were taken in parallel and compared to LiDAR CHM estimates. Stepwise multiple regression was used to estimate biomass. Analysis of variance (ANOVA) tests and pairwise comparisons were used to determine differences between ecotypes and drought stress treatments. We found a significant relationship between the sensor readings and manually measured crop height and biomass, with determination coefficients of 0.73 and 0.71 for height and biomass, respectively. Differences in crop heights were detected more precisely from LiDAR estimates than from manual measurement. Crop biomass differences were also more evident in LiDAR estimates, suggesting differences in ecotypes’ productivity and tolerance to drought. Based on these results, application of the presented UAV-LiDAR workflow will provide new opportunities in assessing bioenergy crop morpho-physiological traits and in delivering improved genotypes for biorefining.</jats:p

Forest defoliator outbreaks alter nutrient cycling in northern waters.

Insect defoliators alter biogeochemical cycles from land into receiving waters by consuming terrestrial biomass and releasing biolabile frass. Here, we related insect outbreaks to water chemistry across 12 boreal lake catchments over 32-years. We report, on average, 27% lower dissolved organic carbon (DOC) and 112% higher dissolved inorganic nitrogen (DIN) concentrations in lake waters when defoliators covered entire catchments and reduced leaf area. DOC reductions reached 32% when deciduous stands dominated. Within-year changes in DOC from insect outbreaks exceeded 86% of between-year trends across a larger dataset of 266 boreal and north temperate lakes from 1990 to 2016. Similarly, within-year increases in DIN from insect outbreaks exceeded local, between-year changes in DIN by 12-times, on average. As insect defoliator outbreaks occur at least every 5 years across a wider 439,661 km2 boreal ecozone of Ontario, we suggest they are an underappreciated driver of biogeochemical cycles in forest catchments of this region.Natural Environment Research Council (NE/L006561/1) Ontario Centres of Excellence (OCE/27649) Natural Sciences and Engineering Research Council of Canada (NSERC/509182-17

Resilience of Spanish forests to recent droughts and climate change

A widespread increase in forest cover is underway in northern Mediterranean forests because of land abandonment and decreased wood demand, but the resilience of these successional forests to climate change remains unresolved. Here we use 18-year time series of canopy greenness—that is, NDVI data derived from satellite imagery—to evaluate the impacts of climate change on Spain's forests. Specifically, we analyzed how NDVI was influenced by the climatic water balance (i.e. Standardized Precipitation Evapotranspiration Index, SPEI), using monthly time-series extracted from 3,100 pixels of forest, categorized into ten forest types. The forests increased in leaf area index by 0.01 per year on average (from 1.7 in 2000 to 1.9 in 2017) but there was enormous variation among years related to climatic water balance. Forest types varied in response to drought events: those dominated by drought-avoiding species showed strong covariance between greenness and SPEI, while those dominated by drought-tolerant species showed weak covariance. Native forests usually recovered more than 80% of greenness within the 18 months and the remainder within 5 years, but plantations of Eucalyptus were less resilient. Management to increase the resilience of forests—a key goal of forestry in the Mediterranean region—appears to have had a positive effect: canopy greenness within protected forests was more resilient to drought than within non-protected forests. In conclusion, many of Spain's successional forests have been resilient to drought over the past 18 years, from the perspective of space. Future studies will need to combine remote sensing with field-based analyses of physiological tolerances and mortality processes to understand how Mediterranean forests will respond to the rapid climate change predicted for this region in the coming decades.Cambridge Trust Fun

UAV-Based Thermal Imaging for High-Throughput Field Phenotyping of Black Poplar Response to Drought

Poplars are fast-growing, high-yielding forest tree species, whose cultivation as second-generation biofuel crops is of increasing interest and can efficiently meet emission reduction goals. Yet, breeding elite poplar trees for drought resistance remains a major challenge. Worldwide breeding programs are largely focused on intra/interspecific hybridization, whereby Populus nigra L. is a fundamental parental pool. While high-throughput genotyping has resulted in unprecedented capabilities to rapidly decode complex genetic architecture of plant stress resistance, linking genomics to phenomics is hindered by technically challenging phenotyping. Relying on unmanned aerial vehicle (UAV)-based remote sensing and imaging techniques, high-throughput field phenotyping (HTFP) aims at enabling highly precise and efficient, non-destructive screening of genotype performance in large populations. To efficiently support forest-tree breeding programs, ground-truthing observations should be complemented with standardized HTFP. In this study, we develop a high-resolution (leaf level) HTFP approach to investigate the response to drought of a full-sib F2 partially inbred population (termed here ‘POP6’), whose F1 was obtained from an intraspecific P. nigra controlled cross between genotypes with highly divergent phenotypes. We assessed the effects of two water treatments (well-watered and moderate drought) on a population of 4603 trees (503 genotypes) hosted in two adjacent experimental plots (1.67 ha) by conducting low-elevation (25 m) flights with an aerial drone and capturing 7836 thermal infrared (TIR) images. TIR images were undistorted, georeferenced, and orthorectified to obtain radiometric mosaics. Canopy temperature (Tc) was extracted using two independent semi-automated segmentation techniques, eCognition- and Matlab-based, to avoid the mixed-pixel problem. Overall, results showed that the UAV platform-based thermal imaging enables to effectively assess genotype variability under drought stress conditions. Tc derived from aerial thermal imagery presented a good correlation with ground-truth stomatal conductance (gs) in both segmentation techniques. Interestingly, the HTFP approach was instrumental to detect drought-tolerant response in 25% of the population. This study shows the potential of UAV-based thermal imaging for field phenomics of poplar and other tree species. This is anticipated to have tremendous implications for accelerating forest tree genetic improvement against abiotic stress

Changes in leaf functional traits of rainforest canopy trees associated with an El Niño event in Borneo

El Nino events generate periods of relatively low precipitation, low cloud cover and high temperature over the rainforests of Southeast Asia, but their impact on tree physiology remains poorly understood. Here we use remote sensing and functional trait approaches-commonly used to understand plant acclimation to environmental fluctuations-to evaluate rainforest responses to an El Nino event at a site in northern Borneo. Spaceborne measurements (i.e. normalised difference vegetation index calculated from Moderate Resolution Imaging Spectroradiometer data) show the rainforest canopy greened throughout 2015, coinciding with a strengthening of the El Nino event in Sabah, Malaysia, then lost greenness in early 2016, when the El Nino was at its peak. Leaf chemical and structural traits measured for mature leaves of 65 species (104 branches from 99 tree canopies), during and after this El Nino event revealed that chlorophyll and carotenoid concentrations were 35% higher in mid 2015 than in mid 2016. Foliar concentrations of the nutrients N, P, K and Mg did not vary, suggesting the mineralisation and transportation processes were unaffected by the El Nino event. Leaves contained more phenolics, tannins and cellulose but less Ca and lignin during the El Nino event, with concentration shifts varying strongly among species. These changes in functional traits were also apparent in hyperspectral reflectance data collected using a field spectrometer, particularly in the shortwave infrared region. Leaf-level acclimation and leaf turnover could have driven the trait changes observed. We argue that trees were not water limited in the initial phase of the El Nino event, and responded by flushing new leaves, seen in the canopy greening trend and higher pigment concentrations (associated with young leaves); we argue that high evaporative demand and depleted soil water eventually caused leaves to drop in 2016. However, further studies are needed to confirm these ideas. Time-series of vegetation dynamics obtained from space can only be understood if changes in functional traits, as well as the quantity of leaves in canopies, are monitored on the ground.Non peer reviewe

Generality of statistical models for predicting multiple leaf traits of temperate broadleaf deciduous trees across a growth season from hyperspectral reflectance

Field spectroscopy is a powerful tool for monitoring leaf functional traits in situ, but it remains unclear whether universal statistical models can be developed to predict traits from spectral information, or whether re-calibration is necessary as conditions vary. In particular, multiple leaf traits vary simultaneously across growing seasons, and it is an open question whether these temporal changes can be predicted successfully from hyperspectral data. To explore this question, monthly changes in 21 physiochemical leaf traits and hyperspectral reflectanceplant spectra were measured for eight deciduous tree species from the UK. Partial least-squares regression (PLSR) was used to evaluate whether each trait could be predicted from a single PLSR model from reflectance spectra, or whether species- and month-level models were needed. Physiochemical traits and spectra varied greatly over the growing season, although there was less variation among mature leaves harvested between June and September. Importantly, leaf spectroscopy was able to predict seasonal variations of most leaf traits accurately, with accuracies of prediction generally higher for mature leaves. However, for several traits, the PLSR estimation models varied among species, and a single PLSR model could not be used to make accurate species-level predictions. Our findings demonstrate that leaf spectra can successfully predict multiple functional foliar traits through the growing season, establishing one of the fundamentals for monitoring and mapping plant functional diversity in temperate forests from air- and spaceborne imaging spectroscopy.NERC Field Spectroscopy Facilit